Class summary:
Wednesday, 4/30
I wrapped up the discussion of sums of independent random variables by
considering the special case when the r.v.'s all have normal
distribution. In this case the sum is also normally distributed, with
parameters (mu and sigma^2) equal to the sum of the corresponding
parameters of the individual r.v.'s. This fact can be applied to very
easily compute probabilities such as P(X>Y+1) or P(X<2Y) in case
both X and Y have normal distribution. I worked out two examples
(Problems 5 and 6 in 5.3).
Reading Guide to Chapter 5.
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